NTIRE 2025 Challenge on Video Quality Enhancement for Video Conferencing: Datasets, Methods and Results
원문 초록
This paper presents a comprehensive review of the 1st Challenge on Video Quality Enhancement for Video Conferencing held at the NTIRE workshop at CVPR 2025, and highlights the problem statement, datasets, proposed solutions, and results. The aim of this challenge was to design a Video Quality Enhancement (VQE) model to enhance video quality in video conferencing scenarios by (a) improving lighting, (b) enhancing colors, (c) reducing noise, and (d) enhancing sharpness - giving a professional studio-like effect. Participants were given a differentiable Video Quality Assessment (VQA) model, training, and test videos. A total of 91 participants registered for the challenge. We received 10 valid submissions that were evaluated in a crowdsourced framework.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
NTIRE 2025 Challenge on Short-form UGC Video Quality Assessment and Enhancement: Methods and Results
Breaking Annotation Barriers: Generalized Video Quality Assessment via Ranking-based Self-Supervision
FVQ: A Large-Scale Dataset and A LMM-based Method for Face Video Quality Assessment
Bridging Video Quality Scoring and Justification via Large Multimodal Models
EyeSim-VQA: A Free-Energy-Guided Eye Simulation Framework for Video Quality Assessment
TDVE-Assessor: Benchmarking and Evaluating the Quality of Text-Driven Video Editing with LMMs
토론
이 논문과 연결된 포럼 글이 아직 없습니다.